Hi! My name is Daniel Cui and I'm 15. I live in Wilton Connecticut, but I was originally born in Maryland and moved to Washington DC and Virginia before settling down in CT. In Wilton, I went to public school for 8 years and then transferred to Deerfield Academy in Deerfield, Massachusetts as a freshman in high school. As a rising-sophomore, I'm looking forward to continuing my years in DA.
For a long time, my strongest passion has been STEM. Currently, I can't really decide which science is my favorite since biology, physics, and chemistry all equally intrigue me. Definitely, I hope that this experience will help me make the critical decision to further pursue my interests in biology.
At my old school, I was part of the Science Olympiad team and participated in the Boomilever, Rocks and Minerals, and Anatomy and Physiology events. After I intensely studied Anatomy and Physiology and managed to win a gold medal at the state level, I discovered a newfound passion for biology. In addition to Science Olympiad, I was also a part of my local robotics team and led the 3-D printing and design team. Though we are only a rookie team, we've advanced to the state level twice out of our three years as a team.
Once I transferred to Deerfield however, I could no longer pursue my interests in STEM through Science Olympiad and the robotics team. As a result, I diverted my attention to Science Bowl, AMC 10, and the Biology Olympiad. Through these new activities, I was able to deepen my love for STEM. At the end of my freshman year, I also started a Physics Olympiad team which I hope will be successful next year.
When I'm not busy pursuing my passion for math and science, I play the viola, swim for my school, read, and play chess. Several weeks ago, I also had the privilege of going to the Dominican Republic for a week with my school to build a house for a family in need which was very exciting.
In anxious anticipation, I'm looking forward to having a great time learning about biology and DNA specifically. Excited to meet everyone there!
Ever since the nascent of biology in ancient times, researchers have been attempting to elucidate the myriad of unanswered and fascinating questions posed by nature. Though there has already been an immense amount of significant discoveries in this field, the study of life still has a long way to go. Initially dominated by researchers like Gregor Mendel and Watson and Crick, biology has branched off from being a scientific field to one that involves aspects that are more similar to engineering. This path was opened to an astonished world in the late twentieth century when scientists were able to introduce recombinant DNA into bacteria to produce the first ever synthetic cell. From there, synthetic biology’s popularity suddenly grew exponentially as a newfound curiosity was born and new questions and opportunities for discovery arose. Of the many instrumental and groundbreaking findings in synthetic biology, the revelation that bacteria could be manipulated for computation to solve intricate math problems gave birth to bacterial computing, namely using bacteria to solve the Hamiltonian Path Problem. Because of this revolutionary discovery, biologists and engineers alike have also found ways to utilize bacteria’s computational power to diagnose cancer, leading to more efficient methods of diagnosis.
Hamiltonian Path Problem One of the foremost examples of using bacteria to do computation, engineering bacteria to solve the Hamiltonian Path Problem was a critical first step in analyzing the ways in which bacteria can be used for computation. Essentially, a Hamiltonian Path Problem is a problem in the mathematical field of graph theory that revolves around determining whether or not there is a path that goes through every vertex once in a directed or undirected graph. The HPP is also considered a NP-complete problem or a nondeterministic polynomial which means that it has solutions that, once found, can be easily proven to be correct. Alfred Adleman was the first person to engineer E coli bacteria to execute this feat by solving a Hamiltonian Path Problem (HPP) that had seven nodes. A few other researchers and engineers have also attempted to solve this problem. However, instead of using a seven node directed graph, they decided to solve a HPP with three nodes. In order to solve the HPP, the researchers had to convert E coli bacteria into computers by building gene circuits to execute an algorithm. In addition, the bacteria had to be sensitive to their environment, and the result had to be able to be observed. To find the solution, the three nodes with three edges in the directed graph were encoded as DNA fragments that were randomly shuffled inside the bacteria with a recombination system. Using genes as nodes that produced red and green fluorescent proteins, the researchers then used the phenotypes that were expressed to reflect the random ordering of edges in the graph. When the clones showed success, they would fluoresce yellow which would be a combination of red and green.
13036_2009_Article_37_Fig1_HTML.jpg
Before delving into the actual experiment, it is important to note the advantages of using bacterial computing. In conventional silicon computer algorithms, potential solutions to a certain problem grow combinatorially with the size of the problem. This has been improved by utilizing parallel processing and an increased number of processors. The same effect can be achieved through cell division in bacteria. In addition to being autonomous or independent and flexible in a changing environment to meet the challenges of the problem, bacteria can continuously and exponentially increase the number of processors working on the problem through mitosis. As a consequence, bacterial computing not only has significant implications in solving difficult math problems, but also an immense amount of potential to surpass modern-day computers.
With those advantages in mind, researchers proceeded to establish the design of the bacteria which required a few abstractions. This, inevitably, involved manipulating the bacteria’s genetic make-up. Perhaps the most crucial abstraction, DNA segments were treated as nodes on the directed graph by the team. Flanking each segment were hixC sites that could be reshuffled using Hin recombinase in order to create random orderings and orientations of the edges on the graph. In the second abstraction, the researchers treated all nodes, with the exception of the terminal one, as genes that are split in two. Therefore, the 5 half of a gene for a given node is found on any DNA edge that terminates on the node, and the 3 half of a gene is found on any DNA edge that originates at the node. For the third abstraction, the arrangement of DNA edges were used to represent a solution to the HPP, displaying a new phenotype in the process.
slide_5.jpg
When the team was confident that their design would be able to solve the problem, the group decided to try to solve a simpler three node directed graph for their first biological implementation. In order to express noticeable phenotypes, the RFP and GFP genes were used as nodes and markers. Between these two marker genes, hixC sites were inserted to allow for recombination. These sites would then be bound by Hin recombinase, causing the DNA fragments to flip or invert. Definitely, the researchers could not have simply inserted the hixC sites which are essentially 13 amino acids in the protein-producing genes, since that would destroy the protein’s function. As a result, they had to examine the 3D structure of certain protein candidates for suitable insertion of hixC sites without losing fluoresce. This was able to be done with RFP and GFP which rendered them suitable choices.
Mathematical modelling was then used to examine certain questions about their system and design. They mostly wanted to see whether order and orientation of DNA edges in the starting construct would affect the probability of detecting a HPP solution. Based on the assumption that each reversal of adjacent DNA edges was equally likely, they calculated the probability that any starting configuration would be in the solution state after k flips using a transition matrix. In the HPP experiment, many bacteria would be attempting to find a solution by randomly flipping the genes catalyzed by Hin recombinase. After using a transition matrix, the researchers found that the probability for a four node and three edge graph, for example, converged quickly at 1/48 after 20 flips since there are 48 or 3!*2^3 possible combinations. This could be easily achieved since E coli divided every 20-30 minutes and would exceed 20 flips after 16 hours. In addition to analyzing the probability of finding a solution, the team also examined how many bacteria were required to guarantee at least one cell with a plasmid that contained a HPP solution. In a directed graph with seven nodes for instance, 6 out of the 14 edges would have to be in the proper order and orientation while the other 8 edges could be in any random order and orientation. Therefore, there would be 8!*2^8 or 10,321,920 million possible solutions since there are 8! number of ways to order the 8 edges and 2 ways to orient each edge. To then calculate the probability of getting a solution in any one plasmid, 8!*2^8 would have to be divided by the total number of different orders and orientations which would be 14!*2^14. Assuming that the states of each plasmid are independent and that enough flips have occurred for the uniform distribution of plasmids, the formula 1-(1-p)^m, in which p stands for the probability that any one plasmid may contain a solution and m stands for the number of plasmids, can be used to calculate the probability that at least one of m plasmid will contain a HPP solution. In order to be 99.9% sure that one would get a HPP solution, there would have to then be 1 billion plasmids according to the formula. It is also important to note that the time it takes for the biological computers to evaluate all 14!*2^14 configurations is a constant multiple of log(14!*2^14) or approximately 14log(14) since the number of processors would be increasing exponentially. By contrast, silicon computers would take a constant multiple of 14!*2^14 to go through all of the possibilities.
13036_2009_Article_37_Fig3_HTML.jpg
Certain that they would be able to solve the HPP with their genetically modified E coli, the team commenced their proof-of-concept experiment. Using a three node directed graph that had three edges, a unique Hamiltonian pathway would start at the RFP node and traveled via edge A to GFP and then traveled from GFP to the TT node or a double transcription terminator, which acted as the ending, via edge B. Unlike edges A and B, edge C acted as a detractor by diverting the pathway from the RFP node to the double terminator TT.
13036_2009_Article_37_Fig4_HTML.jpg
With these three edges, DNA constructs were assembled in order to encode a solved HPP solution as a positive control which already had a known result and two unsolved starting configurations. Furthermore, in order to attain a solution, the solution must start at the RFP node and terminate at the GFP node. Because of this specific pathway, edge A must have contained the 3 half of RFP and the 5 half of GFP while edge B must have contained the 3 half of GFP followed by the double transcription terminator. Edge C, which was not a part of the solution pathway, must have had the 3 half of RFP and TT. Moreover, before each of the 5 half gene’s start codons were ribosome binding sites which supported translation.
images
Using the DNA constructs that contained all three edges, three expression cassettes were then made before they applied Hin recombinase to them. Since all of the cassettes began with the RFP node, each cassette started with a T7 bacteriophage RNA polymerase promoter, a ribosome binding site, and the 5 half of the RFP node before the first hixC site. With this arrangement, the edges were added and formed three different constructs, namely ABC, ACB, and BAC. The ABC construct represented one of two solutions and fluoresced yellow because both the RFP and GFP genes were intact and in forward orientation. The other solution was represented by ABC’ because both of the genes were intact and in the forward orientation, making it fluoresce yellow prior to Hin recombination. However, the only difference was that DNA edge C was backwards. The ACB construct only had the RFP gene intact and in the forward orientation without any interruption by transcriptional terminators. However, the GFP gene was not intact; as a consequence, it was expected to only activate its RFP gene, making it fluoresce red. Last but not least, the BAC gene doesn’t have its GFP and RFP genes intact and should not produce any fluorescence. When each of these constructs were inserted into the E coli, the corresponding phenotypes were expressed in this first experiment.
In a separate experiment protocol, the random ordering of edges were produced using HIn-mediated recombination for each of the constructs ABC, ACB, and BAC. The E coli bacteria were first cotransformed with a plasmid conferring ampicillin resistance and containing one of the three constructs and a plasmid conferring tetracycline resistance with a HIn recombinase expression cassette. These bacteria would then be left overnight to grow to isolate the plasmids that contained the Hin-exposed HPP products. After the plasmids were isolated, the DNA was used in a second round of transformation into bacteria that expressed T7 bacteriophage RNA polymerase and was plated on a dish containing only ampicillin. These bacteria were also grown overnight to allow the T7 RNA polymerases to transcribe each plasmid in its final flipped state. Since each colony represented only one transformation event and HIn recombinase was no longer present, each colony contained the same plasmids and, therefore, only one out of the three DNA constructs. After Hin recombination, the bacteria displayed interesting results and phenotypes. Once Hin recombinase flipped and reordered the DNA edges of each of the constructs, 48 combinations were possible. For the ABC construct, the researchers predicted that it would go from fluorescing yellow to fluorescing red or having no fluorescence which were characteristic of the unsolved arrangements. This was present in the bacteria with the ABC construct, but there were a few green colonies. The unusual green colors may have resulted from the failure of the TT node to block transcription when it was reversed. Furthermore, the ACB and BAC constructs were expected to produce a variety of configurations which included a solution that required two flips and three flips respectively. The yellow colonies that were present for both constructs represented the solutions to the HPP. To verify that they actually had the solutions, the researchers used DNA sequencing to determine the genotypes of three yellow colonies from each construct, and all 9 of them had either the DNA sequence of ABC or ABC’. As a consequence, the team of researchers successfully demonstrated that they could genetically modifiy E coli bacteria to solve the Hamiltonian Path Problem for a three node directed graph using two unsolved and different starting configurations. This would then mark the beginnings of bacterial computing.
13036_2009_Article_37_Fig5_HTML.jpg
13036_2009_Article_37_Fig6_HTML.jpg
Design Because of the incredible power of bacterial computing as depicted by the Hamiltonian Path Problem experiment, synthetic biology has evolved immensely and will continuously have far-reaching implications. Now, researchers have been exploiting the bacteria’s ability to execute algorithms and do computation for more practical purposes like responding to certain stimuli in order to diagnose cancer. Similarly, our design involves some concepts related to bacterial computing that’ll be implemented in certain bacteria to detect the presence of stomach cancer cells.
Essentially, stomach cancer is a type of cancer that originates in the mucus-producing cells on the inside lining of the stomach which is known more specifically as adenocarcinoma. Like most other cancers, it begins when a mutation arises in the cell’s DNA and starts to rapidly divide and proliferate. These cells, unlike normal cells, do not normally follow the cell cycle and won’t undergo apoptosis or cell-death. As a result, these malignant and abnormal cells will form tumors in nearby tissue and, when they have sufficiently accumulated and developed, will eventually metastasize and invade other tissues in the body. Often, people are not able to recognize that they have cancer until metastasis has occurred which is when cancer is at its most lethal stage. Therefore, early and efficient diagnosis would improve cancer treatment immensely. Though stomach cancer is not as prevalent in the US than in other parts of the world, it is still a health concern that should not be disregarded, especially in developing countries.
In order to diagnose stomach cancer, we decided to indirectly detect using Bacteroides thetaiotaomicron bacteriato sense the presence of Helicobacter pylori which is another type of bacteria in the stomach. The former is a type of bacteria that primarily resides in the gut and is, in fact, the most abundant type. It is capable of bringing in nondigestible polysaccharides and hydrolyzing them, and it has environment-sensing mechanisms which consist of membrane proteins. Moreover, H pylori is a spiral-shaped bacterium that grows in the mucus layer that coats the inside of the stomach and has coexisted with humans for thousands of years. To survive in the stomach’s harsh environment, H pylori secretes an enzyme called urease which converts urea into ammonia, neutralizing the acidity of its surroundings. Though it does not often cause illnesses in most infected people, it is a major risk factor for peptic ulcer disease and, more importantly, for non-cardia gastric cancer which is a type of gastric adenocarcinoma. Studies have shown that people who have been infected by H pylori have an increased risk for non-cardia gastric cancer. For instance, in a combined analysis of 12 case-control studies of H pylori and gastric cancer, the risk for non-cardia gastric cancer was estimated to be 6 times higher in people infected with H pylori than for uninfected people. This bacteria causes non-cardia gastric cancer (cancer located in parts of the stomach other than the top inch of the stomach near the esophagus)by increasing the levels of a toxin called CagA which alters the structure of the stomach cells to allow the bacteria to live inside the stomach lumen. Only some of the H pylori produce this toxin that is produced by cytotoxin-associated gene A and inject it using a needle-like appendage. When the levels of this toxin reach a certain level and have accumulated enough, H pylori can then cause chronic inflammation which can lead to non-cardia gastric cancer With this information, it was clear that a strong presence of H pylori could be detected using genetically modified Bacteroides thetaiotaomicron bacteria, aiding in the diagnosis of cancer before metastasis can occur.
image001.gif
3591a3a5-3baf-4a03-b530-e218d1261988image3.jpg
(H pylori (left) and bottom: B thetaiotaomicron (right) )
For successful and efficient diagnosis, B thetaiotaomicron will undergo CRISPR in order to take out the genes that make it hostile in environments other than the gut and to insert the GFP gene and promoter. The patient will then ingest the bacteria, allowing it to flow down into the stomach where it will be able to reside and utilize a system of stimuli and response that is correlated with population density called quorum sensing. This system of recognition and response is mostly used by gram-positive and gram negative bacteria. These are bacteria that are able and not able to retain the crystal violet stain respectively in the Gram staining method of bacteria differentiation. Fortunately, B thetaiotaomicron is gram-negative and can utilize quorum sensing to coordinate its GFP gene expression with the presence of H pylori. When H pylori populations have reached a certain threshold or quorum, the abundance of autoinducer-2s, which are signalling molecules, will be able to activate the GFP gene in B thetaiotaomicron. Once the GFP gene of one bacteria has been activated, a gene that produces the B thetaiotaomicron’s own autoinducer-2s will also be activated, leading to activation of other GFP genes in B thetaiotaomicron bacteria. This will eventually cause a bunch of bacteria to fluoresce in solid waste that has been excreted. In this way, diagnosis of non-cardia gastric cancer can be efficiently done without going through scanning machines, biopsies, endoscopies, and other more tedious methods. Also, it can lead to early detection before the lethal stage of metastasis has been reached.
Diagram
Truth Table
Autoinducer-2
250px-AI-2.png
GFP Promoter
IMAGE - Molecule - GFP + FlAsH - 02.gif
1
1
0
0
Alternate Methods Some alternate methods include endoscopies and endoscopic ultrasounds, in which probes are inserted through the mouth and used to check for the presence or spread of cancer. If abnormal growth or severe inflammation is present, biopsies can then be performed to yield a definite result. Essentially, the doctor will remove a part of the area that is affected and will analyze it for presence of cancerous growth. However these methods are not always useful for early detection before bacteria are able to metastasize. Furthermore, MIT has recently used engineered bacteria that can penetrate and grow in the tumor’s environment in order to express gene for a lacZ enzyme which acts on galactose that is linked to luciferin (already injected into patient). They implemented this design into E coli so that, once the galactose has been cleaved, it will be able to be detected in urine, signifying that the patient has developed cancer.
Conclusion As one could see, bacterial computing not only has huge implications in solving difficult math problems, but it also has significant applications in helping to address some of the world's most pressing health issues like cancer. As this field continues to grow along with synthetic biology, major and revolutionary discoveries will inevitably be made leading to huge advancements in society and in science. Maybe, one day, computers could rely entirely on genetically engineered bacteria. But, for now, it is safe to say that our modern-day silicon MacBooks and Microsoft computers will continue to dominate twenty-first century technology.
For a long time, my strongest passion has been STEM. Currently, I can't really decide which science is my favorite since biology, physics, and chemistry all equally intrigue me. Definitely, I hope that this experience will help me make the critical decision to further pursue my interests in biology.
At my old school, I was part of the Science Olympiad team and participated in the Boomilever, Rocks and Minerals, and Anatomy and Physiology events. After I intensely studied Anatomy and Physiology and managed to win a gold medal at the state level, I discovered a newfound passion for biology. In addition to Science Olympiad, I was also a part of my local robotics team and led the 3-D printing and design team. Though we are only a rookie team, we've advanced to the state level twice out of our three years as a team.
Once I transferred to Deerfield however, I could no longer pursue my interests in STEM through Science Olympiad and the robotics team. As a result, I diverted my attention to Science Bowl, AMC 10, and the Biology Olympiad. Through these new activities, I was able to deepen my love for STEM. At the end of my freshman year, I also started a Physics Olympiad team which I hope will be successful next year.
When I'm not busy pursuing my passion for math and science, I play the viola, swim for my school, read, and play chess. Several weeks ago, I also had the privilege of going to the Dominican Republic for a week with my school to build a house for a family in need which was very exciting.
In anxious anticipation, I'm looking forward to having a great time learning about biology and DNA specifically. Excited to meet everyone there!
http://images.slideplayer.com/22/6368181/slides/slide_5.jpg
http://images.slideplayer.com/22/6368181/slides/slide_5.jpg
Daniel Cui
BLI Session II
Biological Research
https://docs.google.com/presentation/d/1AvknCVhzUUIt7ICc6IJPqMbVuFom4432Lrr_V8PIVtc/edit (link to Google Slides ppt)
Bacterial Computing
Ever since the nascent of biology in ancient times, researchers have been attempting to elucidate the myriad of unanswered and fascinating questions posed by nature. Though there has already been an immense amount of significant discoveries in this field, the study of life still has a long way to go. Initially dominated by researchers like Gregor Mendel and Watson and Crick, biology has branched off from being a scientific field to one that involves aspects that are more similar to engineering. This path was opened to an astonished world in the late twentieth century when scientists were able to introduce recombinant DNA into bacteria to produce the first ever synthetic cell. From there, synthetic biology’s popularity suddenly grew exponentially as a newfound curiosity was born and new questions and opportunities for discovery arose. Of the many instrumental and groundbreaking findings in synthetic biology, the revelation that bacteria could be manipulated for computation to solve intricate math problems gave birth to bacterial computing, namely using bacteria to solve the Hamiltonian Path Problem. Because of this revolutionary discovery, biologists and engineers alike have also found ways to utilize bacteria’s computational power to diagnose cancer, leading to more efficient methods of diagnosis.
Hamiltonian Path Problem
One of the foremost examples of using bacteria to do computation, engineering bacteria to solve the Hamiltonian Path Problem was a critical first step in analyzing the ways in which bacteria can be used for computation. Essentially, a Hamiltonian Path Problem is a problem in the mathematical field of graph theory that revolves around determining whether or not there is a path that goes through every vertex once in a directed or undirected graph. The HPP is also considered a NP-complete problem or a nondeterministic polynomial which means that it has solutions that, once found, can be easily proven to be correct. Alfred Adleman was the first person to engineer E coli bacteria to execute this feat by solving a Hamiltonian Path Problem (HPP) that had seven nodes. A few other researchers and engineers have also attempted to solve this problem. However, instead of using a seven node directed graph, they decided to solve a HPP with three nodes. In order to solve the HPP, the researchers had to convert E coli bacteria into computers by building gene circuits to execute an algorithm. In addition, the bacteria had to be sensitive to their environment, and the result had to be able to be observed. To find the solution, the three nodes with three edges in the directed graph were encoded as DNA fragments that were randomly shuffled inside the bacteria with a recombination system. Using genes as nodes that produced red and green fluorescent proteins, the researchers then used the phenotypes that were expressed to reflect the random ordering of edges in the graph. When the clones showed success, they would fluoresce yellow which would be a combination of red and green.
Before delving into the actual experiment, it is important to note the advantages of using bacterial computing. In conventional silicon computer algorithms, potential solutions to a certain problem grow combinatorially with the size of the problem. This has been improved by utilizing parallel processing and an increased number of processors. The same effect can be achieved through cell division in bacteria. In addition to being autonomous or independent and flexible in a changing environment to meet the challenges of the problem, bacteria can continuously and exponentially increase the number of processors working on the problem through mitosis. As a consequence, bacterial computing not only has significant implications in solving difficult math problems, but also an immense amount of potential to surpass modern-day computers.
With those advantages in mind, researchers proceeded to establish the design of the bacteria which required a few abstractions. This, inevitably, involved manipulating the bacteria’s genetic make-up. Perhaps the most crucial abstraction, DNA segments were treated as nodes on the directed graph by the team. Flanking each segment were hixC sites that could be reshuffled using Hin recombinase in order to create random orderings and orientations of the edges on the graph. In the second abstraction, the researchers treated all nodes, with the exception of the terminal one, as genes that are split in two. Therefore, the 5 half of a gene for a given node is found on any DNA edge that terminates on the node, and the 3 half of a gene is found on any DNA edge that originates at the node. For the third abstraction, the arrangement of DNA edges were used to represent a solution to the HPP, displaying a new phenotype in the process.
When the team was confident that their design would be able to solve the problem, the group decided to try to solve a simpler three node directed graph for their first biological implementation. In order to express noticeable phenotypes, the RFP and GFP genes were used as nodes and markers. Between these two marker genes, hixC sites were inserted to allow for recombination. These sites would then be bound by Hin recombinase, causing the DNA fragments to flip or invert. Definitely, the researchers could not have simply inserted the hixC sites which are essentially 13 amino acids in the protein-producing genes, since that would destroy the protein’s function. As a result, they had to examine the 3D structure of certain protein candidates for suitable insertion of hixC sites without losing fluoresce. This was able to be done with RFP and GFP which rendered them suitable choices.
Mathematical modelling was then used to examine certain questions about their system and design. They mostly wanted to see whether order and orientation of DNA edges in the starting construct would affect the probability of detecting a HPP solution. Based on the assumption that each reversal of adjacent DNA edges was equally likely, they calculated the probability that any starting configuration would be in the solution state after k flips using a transition matrix. In the HPP experiment, many bacteria would be attempting to find a solution by randomly flipping the genes catalyzed by Hin recombinase. After using a transition matrix, the researchers found that the probability for a four node and three edge graph, for example, converged quickly at 1/48 after 20 flips since there are 48 or 3!*2^3 possible combinations. This could be easily achieved since E coli divided every 20-30 minutes and would exceed 20 flips after 16 hours. In addition to analyzing the probability of finding a solution, the team also examined how many bacteria were required to guarantee at least one cell with a plasmid that contained a HPP solution. In a directed graph with seven nodes for instance, 6 out of the 14 edges would have to be in the proper order and orientation while the other 8 edges could be in any random order and orientation. Therefore, there would be 8!*2^8 or 10,321,920 million possible solutions since there are 8! number of ways to order the 8 edges and 2 ways to orient each edge. To then calculate the probability of getting a solution in any one plasmid, 8!*2^8 would have to be divided by the total number of different orders and orientations which would be 14!*2^14. Assuming that the states of each plasmid are independent and that enough flips have occurred for the uniform distribution of plasmids, the formula 1-(1-p)^m, in which p stands for the probability that any one plasmid may contain a solution and m stands for the number of plasmids, can be used to calculate the probability that at least one of m plasmid will contain a HPP solution. In order to be 99.9% sure that one would get a HPP solution, there would have to then be 1 billion plasmids according to the formula. It is also important to note that the time it takes for the biological computers to evaluate all 14!*2^14 configurations is a constant multiple of log(14!*2^14) or approximately 14log(14) since the number of processors would be increasing exponentially. By contrast, silicon computers would take a constant multiple of 14!*2^14 to go through all of the possibilities.
Certain that they would be able to solve the HPP with their genetically modified E coli, the team commenced their proof-of-concept experiment. Using a three node directed graph that had three edges, a unique Hamiltonian pathway would start at the RFP node and traveled via edge A to GFP and then traveled from GFP to the TT node or a double transcription terminator, which acted as the ending, via edge B. Unlike edges A and B, edge C acted as a detractor by diverting the pathway from the RFP node to the double terminator TT.
With these three edges, DNA constructs were assembled in order to encode a solved HPP solution as a positive control which already had a known result and two unsolved starting configurations. Furthermore, in order to attain a solution, the solution must start at the RFP node and terminate at the GFP node. Because of this specific pathway, edge A must have contained the 3 half of RFP and the 5 half of GFP while edge B must have contained the 3 half of GFP followed by the double transcription terminator. Edge C, which was not a part of the solution pathway, must have had the 3 half of RFP and TT. Moreover, before each of the 5 half gene’s start codons were ribosome binding sites which supported translation.
Using the DNA constructs that contained all three edges, three expression cassettes were then made before they applied Hin recombinase to them. Since all of the cassettes began with the RFP node, each cassette started with a T7 bacteriophage RNA polymerase promoter, a ribosome binding site, and the 5 half of the RFP node before the first hixC site. With this arrangement, the edges were added and formed three different constructs, namely ABC, ACB, and BAC. The ABC construct represented one of two solutions and fluoresced yellow because both the RFP and GFP genes were intact and in forward orientation. The other solution was represented by ABC’ because both of the genes were intact and in the forward orientation, making it fluoresce yellow prior to Hin recombination. However, the only difference was that DNA edge C was backwards. The ACB construct only had the RFP gene intact and in the forward orientation without any interruption by transcriptional terminators. However, the GFP gene was not intact; as a consequence, it was expected to only activate its RFP gene, making it fluoresce red. Last but not least, the BAC gene doesn’t have its GFP and RFP genes intact and should not produce any fluorescence. When each of these constructs were inserted into the E coli, the corresponding phenotypes were expressed in this first experiment.
In a separate experiment protocol, the random ordering of edges were produced using HIn-mediated recombination for each of the constructs ABC, ACB, and BAC. The E coli bacteria were first cotransformed with a plasmid conferring ampicillin resistance and containing one of the three constructs and a plasmid conferring tetracycline resistance with a HIn recombinase expression cassette. These bacteria would then be left overnight to grow to isolate the plasmids that contained the Hin-exposed HPP products. After the plasmids were isolated, the DNA was used in a second round of transformation into bacteria that expressed T7 bacteriophage RNA polymerase and was plated on a dish containing only ampicillin. These bacteria were also grown overnight to allow the T7 RNA polymerases to transcribe each plasmid in its final flipped state. Since each colony represented only one transformation event and HIn recombinase was no longer present, each colony contained the same plasmids and, therefore, only one out of the three DNA constructs.
After Hin recombination, the bacteria displayed interesting results and phenotypes. Once Hin recombinase flipped and reordered the DNA edges of each of the constructs, 48 combinations were possible. For the ABC construct, the researchers predicted that it would go from fluorescing yellow to fluorescing red or having no fluorescence which were characteristic of the unsolved arrangements. This was present in the bacteria with the ABC construct, but there were a few green colonies. The unusual green colors may have resulted from the failure of the TT node to block transcription when it was reversed. Furthermore, the ACB and BAC constructs were expected to produce a variety of configurations which included a solution that required two flips and three flips respectively. The yellow colonies that were present for both constructs represented the solutions to the HPP. To verify that they actually had the solutions, the researchers used DNA sequencing to determine the genotypes of three yellow colonies from each construct, and all 9 of them had either the DNA sequence of ABC or ABC’. As a consequence, the team of researchers successfully demonstrated that they could genetically modifiy E coli bacteria to solve the Hamiltonian Path Problem for a three node directed graph using two unsolved and different starting configurations. This would then mark the beginnings of bacterial computing.
Design
Because of the incredible power of bacterial computing as depicted by the Hamiltonian Path Problem experiment, synthetic biology has evolved immensely and will continuously have far-reaching implications. Now, researchers have been exploiting the bacteria’s ability to execute algorithms and do computation for more practical purposes like responding to certain stimuli in order to diagnose cancer. Similarly, our design involves some concepts related to bacterial computing that’ll be implemented in certain bacteria to detect the presence of stomach cancer cells.
Essentially, stomach cancer is a type of cancer that originates in the mucus-producing cells on the inside lining of the stomach which is known more specifically as adenocarcinoma. Like most other cancers, it begins when a mutation arises in the cell’s DNA and starts to rapidly divide and proliferate. These cells, unlike normal cells, do not normally follow the cell cycle and won’t undergo apoptosis or cell-death. As a result, these malignant and abnormal cells will form tumors in nearby tissue and, when they have sufficiently accumulated and developed, will eventually metastasize and invade other tissues in the body. Often, people are not able to recognize that they have cancer until metastasis has occurred which is when cancer is at its most lethal stage. Therefore, early and efficient diagnosis would improve cancer treatment immensely. Though stomach cancer is not as prevalent in the US than in other parts of the world, it is still a health concern that should not be disregarded, especially in developing countries.
In order to diagnose stomach cancer, we decided to indirectly detect using Bacteroides thetaiotaomicron bacteriato sense the presence of Helicobacter pylori which is another type of bacteria in the stomach. The former is a type of bacteria that primarily resides in the gut and is, in fact, the most abundant type. It is capable of bringing in nondigestible polysaccharides and hydrolyzing them, and it has environment-sensing mechanisms which consist of membrane proteins. Moreover, H pylori is a spiral-shaped bacterium that grows in the mucus layer that coats the inside of the stomach and has coexisted with humans for thousands of years. To survive in the stomach’s harsh environment, H pylori secretes an enzyme called urease which converts urea into ammonia, neutralizing the acidity of its surroundings. Though it does not often cause illnesses in most infected people, it is a major risk factor for peptic ulcer disease and, more importantly, for non-cardia gastric cancer which is a type of gastric adenocarcinoma. Studies have shown that people who have been infected by H pylori have an increased risk for non-cardia gastric cancer. For instance, in a combined analysis of 12 case-control studies of H pylori and gastric cancer, the risk for non-cardia gastric cancer was estimated to be 6 times higher in people infected with H pylori than for uninfected people. This bacteria causes non-cardia gastric cancer (cancer located in parts of the stomach other than the top inch of the stomach near the esophagus)by increasing the levels of a toxin called CagA which alters the structure of the stomach cells to allow the bacteria to live inside the stomach lumen. Only some of the H pylori produce this toxin that is produced by cytotoxin-associated gene A and inject it using a needle-like appendage. When the levels of this toxin reach a certain level and have accumulated enough, H pylori can then cause chronic inflammation which can lead to non-cardia gastric cancer With this information, it was clear that a strong presence of H pylori could be detected using genetically modified Bacteroides thetaiotaomicron bacteria, aiding in the diagnosis of cancer before metastasis can occur.
(H pylori (left) and bottom: B thetaiotaomicron (right) )
For successful and efficient diagnosis, B thetaiotaomicron will undergo CRISPR in order to take out the genes that make it hostile in environments other than the gut and to insert the GFP gene and promoter. The patient will then ingest the bacteria, allowing it to flow down into the stomach where it will be able to reside and utilize a system of stimuli and response that is correlated with population density called quorum sensing. This system of recognition and response is mostly used by gram-positive and gram negative bacteria. These are bacteria that are able and not able to retain the crystal violet stain respectively in the Gram staining method of bacteria differentiation. Fortunately, B thetaiotaomicron is gram-negative and can utilize quorum sensing to coordinate its GFP gene expression with the presence of H pylori. When H pylori populations have reached a certain threshold or quorum, the abundance of autoinducer-2s, which are signalling molecules, will be able to activate the GFP gene in B thetaiotaomicron. Once the GFP gene of one bacteria has been activated, a gene that produces the B thetaiotaomicron’s own autoinducer-2s will also be activated, leading to activation of other GFP genes in B thetaiotaomicron bacteria. This will eventually cause a bunch of bacteria to fluoresce in solid waste that has been excreted. In this way, diagnosis of non-cardia gastric cancer can be efficiently done without going through scanning machines, biopsies, endoscopies, and other more tedious methods. Also, it can lead to early detection before the lethal stage of metastasis has been reached.
Diagram
Truth Table
Alternate Methods
Some alternate methods include endoscopies and endoscopic ultrasounds, in which probes are inserted through the mouth and used to check for the presence or spread of cancer. If abnormal growth or severe inflammation is present, biopsies can then be performed to yield a definite result. Essentially, the doctor will remove a part of the area that is affected and will analyze it for presence of cancerous growth. However these methods are not always useful for early detection before bacteria are able to metastasize. Furthermore, MIT has recently used engineered bacteria that can penetrate and grow in the tumor’s environment in order to express gene for a lacZ enzyme which acts on galactose that is linked to luciferin (already injected into patient). They implemented this design into E coli so that, once the galactose has been cleaved, it will be able to be detected in urine, signifying that the patient has developed cancer.
Conclusion
As one could see, bacterial computing not only has huge implications in solving difficult math problems, but it also has significant applications in helping to address some of the world's most pressing health issues like cancer. As this field continues to grow along with synthetic biology, major and revolutionary discoveries will inevitably be made leading to huge advancements in society and in science. Maybe, one day, computers could rely entirely on genetically engineered bacteria. But, for now, it is safe to say that our modern-day silicon MacBooks and Microsoft computers will continue to dominate twenty-first century technology.
Sources